Unlock the power of AI-powered causal inference to drive pricing efficiency while reinforcing your core pricing strategy. These are the methods behind eight- and nine-figure profit gains at some of the world’s largest online businesses — engineered for your stack, owned by you.
One optimization logic — marginal return per dollar — applied to what you charge and what you bid.
Estimate how demand responds to price — granularly, by product and segment. This is the foundation for every pricing and discounting decision, from initial price setting to how deep a markdown can go before it stops paying for itself.
Target discounts where they create the most impact and pull them back where they don’t. Most discount budgets are spread too evenly; elasticity tells you where a cut actually moves volume worth having.
Marginal-ROI price and discount setting across markets and categories, calibrated to protect revenue. The approach behind tens of millions in validated EBIT improvement at enterprise scale.
Forecast overstock risk and plan discounts to maximize value over a unit’s life — and use elasticity to build safer stock positions you can move with smaller cuts if needed.
Match where it matters, and don’t cut price just because a competitor is clearing overstock. We rebuild matching logic around true ROI, calibrated to ensure zero unnecessary revenue loss.
The big one: move from a manual, rule-of-thumb pricing process to a modular, tunable algorithmic system — separated into supervised learning, reinforcement learning, and control surfaces so each can be tested and improved independently.
We support the three most common pricing models — with elasticity-based initial price setting and algorithmic discounting underneath each.
Competitive, stable pricing — where the game is hitting benchmarks efficiently, not cutting deeper than you need to.
Regular price with promotional dips — where timing, depth, and product inclusion decide whether promotions build margin or bleed it.
Sell-through over a season with sale events — where early, shallow action on at-risk stock beats deep end-of-season cuts.
The opportunities — and the money quietly lost — depend on which model you run. Here’s where we focus.
It’s easy to measure the sales you gain during low periods — and easy to miss the sales you lose during the high ones. Optimal timing of promotions and which products to exclude are first-order efficiency drivers, and depth matters most of all: companies routinely discount too deeply because they underestimate the high-period sales they give up. Move from business rules to data-driven efficiency.
You want to combine competitive pricing data with elasticity-based return curves — but how? Hit your competitive benchmarks with elasticity-based pricing that maximizes bang for buck. Not every product needs to match the market: elasticity tells you where matching wins volume worth having, and where holding price protects margin at no real cost. Competitive where it counts, disciplined everywhere else.
Limit unneeded discounts on items that will sell through at full price, and discount at-risk stock early with smaller cuts to avoid deep end-of-season liquidation. Our algorithmic solutions take the guesswork out — and data-driven storage and re-tagging limit liquidation entirely: if it can be sold next season, hold it. Small cuts early beat big cuts late.
The short version: you know the system could be far more efficient, but technical and strategic blockers are in the way. We bring both — real pricing strategy and the engineering to build it — designing the systems you need and testing them rigorously before they ever touch revenue.
We don’t arrive pitching a piece of software we’ve already built. We start with a deep assessment of your needs and opportunities, size the prize, and right-size the solution to it — then use leading-edge, proven techniques to drive efficiency you can measure and own.
A structured diagnostic of what you can answer today, where the biggest impact sits, and what’s actually worth building. No assumed solution.
Quantify the upside before committing engineering. Complexity is matched to value, never the reverse.
Delivered as a service, or built shoulder-to-shoulder with your teams so the capability stays in-house.
Every result is tested — geo holdouts, incrementality, A/B validation. You keep the model, the code, and the intelligence.
Book an assessment and we’ll map where your marketing and pricing stand today — and where the upside is. We reply within one business day.
Book an assessment →We only take on work where we’re confident we can add tremendous value.